Overview

Dataset statistics

Number of variables29
Number of observations201
Missing cells5
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.7 KiB
Average record size in memory232.6 B

Variable types

Numeric17
Categorical12

Warnings

compression-ratio is highly correlated with diesel and 1 other fieldsHigh correlation
city-mpg is highly correlated with highway-mpg and 1 other fieldsHigh correlation
highway-mpg is highly correlated with city-mpg and 1 other fieldsHigh correlation
city-L/100km is highly correlated with city-mpg and 1 other fieldsHigh correlation
diesel is highly correlated with compression-ratio and 1 other fieldsHigh correlation
gas is highly correlated with compression-ratio and 1 other fieldsHigh correlation
gas is highly correlated with fuel-system and 1 other fieldsHigh correlation
fuel-system is highly correlated with gas and 1 other fieldsHigh correlation
diesel is highly correlated with gas and 1 other fieldsHigh correlation
stroke has 4 (2.0%) missing values Missing
symboling has 65 (32.3%) zeros Zeros

Reproduction

Analysis started2021-03-04 08:17:39.760787
Analysis finished2021-03-04 08:18:02.429829
Duration22.67 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

symboling
Real number (ℝ)

ZEROS

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8407960199
Minimum-2
Maximum3
Zeros65
Zeros (%)32.3%
Memory size1.7 KiB
2021-03-04T03:18:02.479184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.254801723
Coefficient of variation (CV)1.492397315
Kurtosis-0.7071776172
Mean0.8407960199
Median Absolute Deviation (MAD)1
Skewness0.1973703603
Sum169
Variance1.574527363
MonotocityNot monotonic
2021-03-04T03:18:02.534305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
065
32.3%
152
25.9%
232
15.9%
327
13.4%
-122
 
10.9%
-23
 
1.5%
ValueCountFrequency (%)
-23
 
1.5%
-122
 
10.9%
065
32.3%
152
25.9%
232
15.9%
ValueCountFrequency (%)
327
13.4%
232
15.9%
152
25.9%
065
32.3%
-122
 
10.9%

normalized-losses
Real number (ℝ≥0)

Distinct51
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122
Minimum65
Maximum256
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:02.600868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile77
Q1101
median122
Q3137
95-th percentile186
Maximum256
Range191
Interquartile range (IQR)36

Descriptive statistics

Standard deviation31.99624978
Coefficient of variation (CV)0.2622643425
Kurtosis1.319067557
Mean122
Median Absolute Deviation (MAD)21
Skewness0.8465463513
Sum24522
Variance1023.76
MonotocityNot monotonic
2021-03-04T03:18:02.675925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12241
20.4%
16111
 
5.5%
918
 
4.0%
1507
 
3.5%
1046
 
3.0%
1286
 
3.0%
1346
 
3.0%
655
 
2.5%
1685
 
2.5%
945
 
2.5%
Other values (41)101
50.2%
ValueCountFrequency (%)
655
2.5%
745
2.5%
771
 
0.5%
781
 
0.5%
812
 
1.0%
ValueCountFrequency (%)
2561
0.5%
2311
0.5%
1972
1.0%
1942
1.0%
1922
1.0%

make
Categorical

Distinct22
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
32 
nissan
18 
mazda
17 
honda
13 
mitsubishi
13 
Other values (17)
108 

Length

Max length13
Median length6
Mean length6.502487562
Min length3

Characters and Unicode

Total characters1307
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi
ValueCountFrequency (%)
toyota32
15.9%
nissan18
 
9.0%
mazda17
 
8.5%
honda13
 
6.5%
mitsubishi13
 
6.5%
volkswagen12
 
6.0%
subaru12
 
6.0%
volvo11
 
5.5%
peugot11
 
5.5%
dodge9
 
4.5%
Other values (12)53
26.4%
2021-03-04T03:18:02.828149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota32
15.9%
nissan18
 
9.0%
mazda17
 
8.5%
honda13
 
6.5%
mitsubishi13
 
6.5%
volkswagen12
 
6.0%
subaru12
 
6.0%
volvo11
 
5.5%
peugot11
 
5.5%
dodge9
 
4.5%
Other values (12)53
26.4%

Most occurring characters

ValueCountFrequency (%)
a153
 
11.7%
o151
 
11.6%
s106
 
8.1%
t100
 
7.7%
e80
 
6.1%
u71
 
5.4%
n71
 
5.4%
i65
 
5.0%
d62
 
4.7%
m57
 
4.4%
Other values (15)391
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1296
99.2%
Dash Punctuation11
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
a153
11.8%
o151
 
11.7%
s106
 
8.2%
t100
 
7.7%
e80
 
6.2%
u71
 
5.5%
n71
 
5.5%
i65
 
5.0%
d62
 
4.8%
m57
 
4.4%
Other values (14)380
29.3%
ValueCountFrequency (%)
-11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1296
99.2%
Common11
 
0.8%

Most frequent character per script

ValueCountFrequency (%)
a153
11.8%
o151
 
11.7%
s106
 
8.2%
t100
 
7.7%
e80
 
6.2%
u71
 
5.5%
n71
 
5.5%
i65
 
5.0%
d62
 
4.8%
m57
 
4.4%
Other values (14)380
29.3%
ValueCountFrequency (%)
-11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1307
100.0%

Most frequent character per block

ValueCountFrequency (%)
a153
 
11.7%
o151
 
11.6%
s106
 
8.1%
t100
 
7.7%
e80
 
6.1%
u71
 
5.4%
n71
 
5.4%
i65
 
5.0%
d62
 
4.7%
m57
 
4.4%
Other values (15)391
29.9%

aspiration
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
165 
turbo
36 

Length

Max length5
Median length3
Mean length3.358208955
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd
ValueCountFrequency (%)
std165
82.1%
turbo36
 
17.9%
2021-03-04T03:18:03.128753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:03.177057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
std165
82.1%
turbo36
 
17.9%

Most occurring characters

ValueCountFrequency (%)
t201
29.8%
s165
24.4%
d165
24.4%
u36
 
5.3%
r36
 
5.3%
b36
 
5.3%
o36
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter675
100.0%

Most frequent character per category

ValueCountFrequency (%)
t201
29.8%
s165
24.4%
d165
24.4%
u36
 
5.3%
r36
 
5.3%
b36
 
5.3%
o36
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin675
100.0%

Most frequent character per script

ValueCountFrequency (%)
t201
29.8%
s165
24.4%
d165
24.4%
u36
 
5.3%
r36
 
5.3%
b36
 
5.3%
o36
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII675
100.0%

Most frequent character per block

ValueCountFrequency (%)
t201
29.8%
s165
24.4%
d165
24.4%
u36
 
5.3%
r36
 
5.3%
b36
 
5.3%
o36
 
5.3%

num-of-doors
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
115 
two
86 

Length

Max length4
Median length4
Mean length3.572139303
Min length3

Characters and Unicode

Total characters718
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour
ValueCountFrequency (%)
four115
57.2%
two86
42.8%
2021-03-04T03:18:03.282084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:03.322084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
four115
57.2%
two86
42.8%

Most occurring characters

ValueCountFrequency (%)
o201
28.0%
f115
16.0%
u115
16.0%
r115
16.0%
t86
12.0%
w86
12.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter718
100.0%

Most frequent character per category

ValueCountFrequency (%)
o201
28.0%
f115
16.0%
u115
16.0%
r115
16.0%
t86
12.0%
w86
12.0%

Most occurring scripts

ValueCountFrequency (%)
Latin718
100.0%

Most frequent character per script

ValueCountFrequency (%)
o201
28.0%
f115
16.0%
u115
16.0%
r115
16.0%
t86
12.0%
w86
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII718
100.0%

Most frequent character per block

ValueCountFrequency (%)
o201
28.0%
f115
16.0%
u115
16.0%
r115
16.0%
t86
12.0%
w86
12.0%

body-style
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sedan
94 
hatchback
68 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.611940299
Min length5

Characters and Unicode

Total characters1329
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan
ValueCountFrequency (%)
sedan94
46.8%
hatchback68
33.8%
wagon25
 
12.4%
hardtop8
 
4.0%
convertible6
 
3.0%
2021-03-04T03:18:03.427558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:03.471263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
sedan94
46.8%
hatchback68
33.8%
wagon25
 
12.4%
hardtop8
 
4.0%
convertible6
 
3.0%

Most occurring characters

ValueCountFrequency (%)
a263
19.8%
h144
10.8%
c142
10.7%
n125
9.4%
e106
8.0%
d102
 
7.7%
s94
 
7.1%
t82
 
6.2%
b74
 
5.6%
k68
 
5.1%
Other values (8)129
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1329
100.0%

Most frequent character per category

ValueCountFrequency (%)
a263
19.8%
h144
10.8%
c142
10.7%
n125
9.4%
e106
8.0%
d102
 
7.7%
s94
 
7.1%
t82
 
6.2%
b74
 
5.6%
k68
 
5.1%
Other values (8)129
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1329
100.0%

Most frequent character per script

ValueCountFrequency (%)
a263
19.8%
h144
10.8%
c142
10.7%
n125
9.4%
e106
8.0%
d102
 
7.7%
s94
 
7.1%
t82
 
6.2%
b74
 
5.6%
k68
 
5.1%
Other values (8)129
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1329
100.0%

Most frequent character per block

ValueCountFrequency (%)
a263
19.8%
h144
10.8%
c142
10.7%
n125
9.4%
e106
8.0%
d102
 
7.7%
s94
 
7.1%
t82
 
6.2%
b74
 
5.6%
k68
 
5.1%
Other values (8)129
9.7%

drive-wheels
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
fwd
118 
rwd
75 
4wd
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd
ValueCountFrequency (%)
fwd118
58.7%
rwd75
37.3%
4wd8
 
4.0%
2021-03-04T03:18:03.588526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:03.629242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
fwd118
58.7%
rwd75
37.3%
4wd8
 
4.0%

Most occurring characters

ValueCountFrequency (%)
w201
33.3%
d201
33.3%
f118
19.6%
r75
 
12.4%
48
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter595
98.7%
Decimal Number8
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
w201
33.8%
d201
33.8%
f118
19.8%
r75
 
12.6%
ValueCountFrequency (%)
48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin595
98.7%
Common8
 
1.3%

Most frequent character per script

ValueCountFrequency (%)
w201
33.8%
d201
33.8%
f118
19.8%
r75
 
12.6%
ValueCountFrequency (%)
48
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII603
100.0%

Most frequent character per block

ValueCountFrequency (%)
w201
33.3%
d201
33.3%
f118
19.6%
r75
 
12.4%
48
 
1.3%

engine-location
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
front
198 
rear
 
3

Length

Max length5
Median length5
Mean length4.985074627
Min length4

Characters and Unicode

Total characters1002
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront
ValueCountFrequency (%)
front198
98.5%
rear3
 
1.5%
2021-03-04T03:18:03.742955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:03.788722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
front198
98.5%
rear3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r204
20.4%
f198
19.8%
o198
19.8%
n198
19.8%
t198
19.8%
e3
 
0.3%
a3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1002
100.0%

Most frequent character per category

ValueCountFrequency (%)
r204
20.4%
f198
19.8%
o198
19.8%
n198
19.8%
t198
19.8%
e3
 
0.3%
a3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1002
100.0%

Most frequent character per script

ValueCountFrequency (%)
r204
20.4%
f198
19.8%
o198
19.8%
n198
19.8%
t198
19.8%
e3
 
0.3%
a3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1002
100.0%

Most frequent character per block

ValueCountFrequency (%)
r204
20.4%
f198
19.8%
o198
19.8%
n198
19.8%
t198
19.8%
e3
 
0.3%
a3
 
0.3%

wheel-base
Real number (ℝ≥0)

Distinct52
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.79701493
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:03.841083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.066365555
Coefficient of variation (CV)0.06140231625
Kurtosis0.9484450961
Mean98.79701493
Median Absolute Deviation (MAD)2.8
Skewness1.031261443
Sum19858.2
Variance36.80079104
MonotocityNot monotonic
2021-03-04T03:18:03.919315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.720
 
10.0%
94.519
 
9.5%
95.713
 
6.5%
96.58
 
4.0%
97.37
 
3.5%
96.36
 
3.0%
98.46
 
3.0%
107.96
 
3.0%
98.86
 
3.0%
99.16
 
3.0%
Other values (42)104
51.7%
ValueCountFrequency (%)
86.62
1.0%
88.41
 
0.5%
88.62
1.0%
89.53
1.5%
91.32
1.0%
ValueCountFrequency (%)
120.91
 
0.5%
115.62
1.0%
114.24
2.0%
1132
1.0%
1121
 
0.5%

length
Real number (ℝ≥0)

Distinct73
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8371023307
Minimum0.6780394041
Maximum1
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:04.001050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.6780394041
5-th percentile0.755886593
Q10.8015377222
median0.8322921672
Q30.8817876021
95-th percentile0.9466602595
Maximum1
Range0.3219605959
Interquartile range (IQR)0.08024987987

Descriptive statistics

Standard deviation0.05921275873
Coefficient of variation (CV)0.07073538868
Kurtosis-0.06519162777
Mean0.8371023307
Median Absolute Deviation (MAD)0.03315713599
Skewness0.1544463518
Sum168.2575685
Variance0.003506150797
MonotocityNot monotonic
2021-03-04T03:18:04.078524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75588659315
 
7.5%
0.907256126911
 
5.5%
0.82508409427
 
3.5%
0.89716482467
 
3.5%
0.79913503127
 
3.5%
0.85439692466
 
3.0%
0.79432964926
 
3.0%
0.89668428646
 
3.0%
0.84670831336
 
3.0%
0.84959154255
 
2.5%
Other values (63)125
62.2%
ValueCountFrequency (%)
0.67803940411
 
0.5%
0.69485824122
1.0%
0.72080730423
1.5%
0.74915905811
 
0.5%
0.75396444021
 
0.5%
ValueCountFrequency (%)
11
 
0.5%
0.97357039882
1.0%
0.95915425282
1.0%
0.95723211
 
0.5%
0.95579048534
2.0%

width
Real number (ℝ≥0)

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9151257601
Minimum0.8375
Maximum1
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:04.157016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.8375
5-th percentile0.8833333333
Q10.8902777778
median0.9097222222
Q30.925
95-th percentile0.9763888889
Maximum1
Range0.1625
Interquartile range (IQR)0.03472222222

Descriptive statistics

Standard deviation0.0291870947
Coefficient of variation (CV)0.03189408055
Kurtosis0.6786551692
Mean0.9151257601
Median Absolute Deviation (MAD)0.01944444444
Skewness0.8750290419
Sum183.9402778
Variance0.0008518864973
MonotocityNot monotonic
2021-03-04T03:18:04.231200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.886111111124
 
11.9%
0.923611111123
 
11.4%
0.908333333315
 
7.5%
0.9510
 
5.0%
0.894444444410
 
5.0%
0.88888888899
 
4.5%
0.88333333339
 
4.5%
0.90972222228
 
4.0%
0.90555555567
 
3.5%
0.93333333336
 
3.0%
Other values (33)80
39.8%
ValueCountFrequency (%)
0.83751
 
0.5%
0.85833333331
 
0.5%
0.86805555561
 
0.5%
0.88055555561
 
0.5%
0.88333333339
4.5%
ValueCountFrequency (%)
11
 
0.5%
0.99583333333
1.5%
0.99166666673
1.5%
0.98472222221
 
0.5%
0.98055555561
 
0.5%

height
Real number (ℝ≥0)

Distinct49
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.76666667
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:04.309817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.447822161
Coefficient of variation (CV)0.04552676059
Kurtosis-0.4329081504
Mean53.76666667
Median Absolute Deviation (MAD)1.6
Skewness0.02917329915
Sum10807.1
Variance5.991833333
MonotocityNot monotonic
2021-03-04T03:18:04.391471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.814
 
7.0%
55.712
 
6.0%
54.510
 
5.0%
54.110
 
5.0%
529
 
4.5%
55.59
 
4.5%
56.78
 
4.0%
54.38
 
4.0%
51.67
 
3.5%
56.17
 
3.5%
Other values (39)107
53.2%
ValueCountFrequency (%)
47.81
 
0.5%
48.82
1.0%
49.42
1.0%
49.64
2.0%
49.73
1.5%
ValueCountFrequency (%)
59.82
1.0%
59.13
1.5%
58.74
2.0%
58.31
 
0.5%
57.53
1.5%

curb-weight
Real number (ℝ≥0)

Distinct169
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.666667
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:04.468357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1905
Q12169
median2414
Q32926
95-th percentile3505
Maximum4066
Range2578
Interquartile range (IQR)757

Descriptive statistics

Standard deviation517.2967266
Coefficient of variation (CV)0.2024116577
Kurtosis0.03491557605
Mean2555.666667
Median Absolute Deviation (MAD)377
Skewness0.7058035875
Sum513689
Variance267595.9033
MonotocityNot monotonic
2021-03-04T03:18:04.552085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23854
 
2.0%
19183
 
1.5%
19893
 
1.5%
22753
 
1.5%
24142
 
1.0%
21452
 
1.0%
23952
 
1.0%
23372
 
1.0%
24032
 
1.0%
24102
 
1.0%
Other values (159)176
87.6%
ValueCountFrequency (%)
14881
0.5%
17131
0.5%
18191
0.5%
18371
0.5%
18741
0.5%
ValueCountFrequency (%)
40662
1.0%
39501
0.5%
39001
0.5%
37701
0.5%
37501
0.5%

engine-type
Categorical

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
145 
ohcf
15 
ohcv
 
13
l
 
12
dohc
 
12

Length

Max length5
Median length3
Mean length3.119402985
Min length1

Characters and Unicode

Total characters627
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc
ValueCountFrequency (%)
ohc145
72.1%
ohcf15
 
7.5%
ohcv13
 
6.5%
l12
 
6.0%
dohc12
 
6.0%
rotor4
 
2.0%
2021-03-04T03:18:04.693583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:04.740946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ohc145
72.1%
ohcf15
 
7.5%
ohcv13
 
6.5%
l12
 
6.0%
dohc12
 
6.0%
rotor4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o193
30.8%
h185
29.5%
c185
29.5%
f15
 
2.4%
v13
 
2.1%
d12
 
1.9%
l12
 
1.9%
r8
 
1.3%
t4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter627
100.0%

Most frequent character per category

ValueCountFrequency (%)
o193
30.8%
h185
29.5%
c185
29.5%
f15
 
2.4%
v13
 
2.1%
d12
 
1.9%
l12
 
1.9%
r8
 
1.3%
t4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin627
100.0%

Most frequent character per script

ValueCountFrequency (%)
o193
30.8%
h185
29.5%
c185
29.5%
f15
 
2.4%
v13
 
2.1%
d12
 
1.9%
l12
 
1.9%
r8
 
1.3%
t4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII627
100.0%

Most frequent character per block

ValueCountFrequency (%)
o193
30.8%
h185
29.5%
c185
29.5%
f15
 
2.4%
v13
 
2.1%
d12
 
1.9%
l12
 
1.9%
r8
 
1.3%
t4
 
0.6%

num-of-cylinders
Categorical

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
157 
six
24 
five
 
10
eight
 
4
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.895522388
Min length3

Characters and Unicode

Total characters783
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive
ValueCountFrequency (%)
four157
78.1%
six24
 
11.9%
five10
 
5.0%
eight4
 
2.0%
two4
 
2.0%
twelve1
 
0.5%
three1
 
0.5%
2021-03-04T03:18:04.871097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:04.917816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
four157
78.1%
six24
 
11.9%
five10
 
5.0%
eight4
 
2.0%
two4
 
2.0%
twelve1
 
0.5%
three1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f167
21.3%
o161
20.6%
r158
20.2%
u157
20.1%
i38
 
4.9%
s24
 
3.1%
x24
 
3.1%
e18
 
2.3%
v11
 
1.4%
t10
 
1.3%
Other values (4)15
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter783
100.0%

Most frequent character per category

ValueCountFrequency (%)
f167
21.3%
o161
20.6%
r158
20.2%
u157
20.1%
i38
 
4.9%
s24
 
3.1%
x24
 
3.1%
e18
 
2.3%
v11
 
1.4%
t10
 
1.3%
Other values (4)15
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin783
100.0%

Most frequent character per script

ValueCountFrequency (%)
f167
21.3%
o161
20.6%
r158
20.2%
u157
20.1%
i38
 
4.9%
s24
 
3.1%
x24
 
3.1%
e18
 
2.3%
v11
 
1.4%
t10
 
1.3%
Other values (4)15
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII783
100.0%

Most frequent character per block

ValueCountFrequency (%)
f167
21.3%
o161
20.6%
r158
20.2%
u157
20.1%
i38
 
4.9%
s24
 
3.1%
x24
 
3.1%
e18
 
2.3%
v11
 
1.4%
t10
 
1.3%
Other values (4)15
 
1.9%

engine-size
Real number (ℝ≥0)

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.8756219
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:04.982448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q198
median120
Q3141
95-th percentile194
Maximum326
Range265
Interquartile range (IQR)43

Descriptive statistics

Standard deviation41.54683445
Coefficient of variation (CV)0.3274611295
Kurtosis5.497490767
Mean126.8756219
Median Absolute Deviation (MAD)22
Skewness1.979144197
Sum25502
Variance1726.139453
MonotocityNot monotonic
2021-03-04T03:18:05.056602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
9215
 
7.5%
12215
 
7.5%
9814
 
7.0%
9714
 
7.0%
10813
 
6.5%
11012
 
6.0%
9010
 
5.0%
1098
 
4.0%
1417
 
3.5%
1207
 
3.5%
Other values (33)86
42.8%
ValueCountFrequency (%)
611
 
0.5%
703
 
1.5%
791
 
0.5%
801
 
0.5%
9010
5.0%
ValueCountFrequency (%)
3261
0.5%
3081
0.5%
3041
0.5%
2582
1.0%
2342
1.0%

fuel-system
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
92 
2bbl
64 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.895522388
Min length3

Characters and Unicode

Total characters783
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi
ValueCountFrequency (%)
mpfi92
45.8%
2bbl64
31.8%
idi20
 
10.0%
1bbl11
 
5.5%
spdi9
 
4.5%
4bbl3
 
1.5%
mfi1
 
0.5%
spfi1
 
0.5%
2021-03-04T03:18:05.181429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:05.225846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
mpfi92
45.8%
2bbl64
31.8%
idi20
 
10.0%
1bbl11
 
5.5%
spdi9
 
4.5%
4bbl3
 
1.5%
mfi1
 
0.5%
spfi1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b156
19.9%
i143
18.3%
p102
13.0%
f94
12.0%
m93
11.9%
l78
10.0%
264
8.2%
d29
 
3.7%
111
 
1.4%
s10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter705
90.0%
Decimal Number78
 
10.0%

Most frequent character per category

ValueCountFrequency (%)
b156
22.1%
i143
20.3%
p102
14.5%
f94
13.3%
m93
13.2%
l78
11.1%
d29
 
4.1%
s10
 
1.4%
ValueCountFrequency (%)
264
82.1%
111
 
14.1%
43
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin705
90.0%
Common78
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
b156
22.1%
i143
20.3%
p102
14.5%
f94
13.3%
m93
13.2%
l78
11.1%
d29
 
4.1%
s10
 
1.4%
ValueCountFrequency (%)
264
82.1%
111
 
14.1%
43
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII783
100.0%

Most frequent character per block

ValueCountFrequency (%)
b156
19.9%
i143
18.3%
p102
13.0%
f94
12.0%
m93
11.9%
l78
10.0%
264
8.2%
d29
 
3.7%
111
 
1.4%
s10
 
1.3%

bore
Real number (ℝ≥0)

Distinct39
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.330691567
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:05.294395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.2680718571
Coefficient of variation (CV)0.08048534418
Kurtosis-0.7981931312
Mean3.330691567
Median Absolute Deviation (MAD)0.23
Skewness-0.03273032795
Sum669.469005
Variance0.07186252058
MonotocityNot monotonic
2021-03-04T03:18:05.364206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3.6223
 
11.4%
3.1920
 
10.0%
3.1515
 
7.5%
2.9712
 
6.0%
3.0310
 
5.0%
3.469
 
4.5%
3.788
 
4.0%
3.318
 
4.0%
3.438
 
4.0%
2.917
 
3.5%
Other values (29)81
40.3%
ValueCountFrequency (%)
2.541
 
0.5%
2.681
 
0.5%
2.917
3.5%
2.921
 
0.5%
2.9712
6.0%
ValueCountFrequency (%)
3.941
 
0.5%
3.82
 
1.0%
3.788
4.0%
3.761
 
0.5%
3.743
 
1.5%

stroke
Real number (ℝ≥0)

MISSING

Distinct36
Distinct (%)18.3%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.256903553
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:05.437508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31925624
Coefficient of variation (CV)0.09802446857
Kurtosis2.028784197
Mean3.256903553
Median Absolute Deviation (MAD)0.17
Skewness-0.6937783864
Sum641.61
Variance0.1019245468
MonotocityNot monotonic
2021-03-04T03:18:05.504784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.419
 
9.5%
3.0314
 
7.0%
3.1514
 
7.0%
3.2314
 
7.0%
3.3913
 
6.5%
2.6411
 
5.5%
3.359
 
4.5%
3.299
 
4.5%
3.468
 
4.0%
3.416
 
3.0%
Other values (26)80
39.8%
ValueCountFrequency (%)
2.071
 
0.5%
2.192
 
1.0%
2.361
 
0.5%
2.6411
5.5%
2.682
 
1.0%
ValueCountFrequency (%)
4.172
 
1.0%
3.93
1.5%
3.864
2.0%
3.645
2.5%
3.586
3.0%

compression-ratio
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.16427861
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:05.570500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.9
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation4.004965493
Coefficient of variation (CV)0.3940235848
Kurtosis5.068872476
Mean10.16427861
Median Absolute Deviation (MAD)0.4
Skewness2.584462433
Sum2043.02
Variance16.0397486
MonotocityNot monotonic
2021-03-04T03:18:05.632767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
946
22.9%
9.426
12.9%
8.514
 
7.0%
9.513
 
6.5%
9.311
 
5.5%
8.79
 
4.5%
88
 
4.0%
9.28
 
4.0%
76
 
3.0%
8.65
 
2.5%
Other values (22)55
27.4%
ValueCountFrequency (%)
76
3.0%
7.55
2.5%
7.64
2.0%
7.72
 
1.0%
7.81
 
0.5%
ValueCountFrequency (%)
235
2.5%
22.71
 
0.5%
22.53
1.5%
221
 
0.5%
21.91
 
0.5%

horsepower
Real number (ℝ≥0)

Distinct59
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.4055339
Minimum48
Maximum262
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:05.707430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile176
Maximum262
Range214
Interquartile range (IQR)46

Descriptive statistics

Standard deviation37.3656995
Coefficient of variation (CV)0.3613510621
Kurtosis1.320379508
Mean103.4055339
Median Absolute Deviation (MAD)25
Skewness1.146517326
Sum20784.51232
Variance1396.195499
MonotocityNot monotonic
2021-03-04T03:18:05.779887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6819
 
9.5%
6910
 
5.0%
1169
 
4.5%
709
 
4.5%
1108
 
4.0%
957
 
3.5%
626
 
3.0%
1146
 
3.0%
1016
 
3.0%
886
 
3.0%
Other values (49)115
57.2%
ValueCountFrequency (%)
481
0.5%
522
1.0%
551
0.5%
562
1.0%
581
0.5%
ValueCountFrequency (%)
2621
 
0.5%
2073
1.5%
2001
 
0.5%
1842
1.0%
1823
1.5%

peak-rpm
Real number (ℝ≥0)

Distinct23
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5117.665368
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:05.842388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5125.369458
Q35500
95-th percentile6000
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation478.113805
Coefficient of variation (CV)0.09342420238
Kurtosis0.1075508188
Mean5117.665368
Median Absolute Deviation (MAD)325.3694581
Skewness0.107769971
Sum1028650.739
Variance228592.8106
MonotocityNot monotonic
2021-03-04T03:18:05.904190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
480036
17.9%
550036
17.9%
500027
13.4%
520023
11.4%
540011
 
5.5%
60009
 
4.5%
52507
 
3.5%
58007
 
3.5%
45007
 
3.5%
42005
 
2.5%
Other values (13)33
16.4%
ValueCountFrequency (%)
41505
2.5%
42005
2.5%
42503
1.5%
43504
2.0%
44003
1.5%
ValueCountFrequency (%)
66002
 
1.0%
60009
4.5%
59003
 
1.5%
58007
3.5%
56001
 
0.5%

city-mpg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct29
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.17910448
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:05.967539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.423220469
Coefficient of variation (CV)0.2551012279
Kurtosis0.7539680878
Mean25.17910448
Median Absolute Deviation (MAD)5
Skewness0.6804334707
Sum5061
Variance41.25776119
MonotocityNot monotonic
2021-03-04T03:18:06.032476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3128
13.9%
1927
13.4%
2422
10.9%
2714
 
7.0%
1712
 
6.0%
2612
 
6.0%
2312
 
6.0%
218
 
4.0%
308
 
4.0%
258
 
4.0%
Other values (19)50
24.9%
ValueCountFrequency (%)
131
 
0.5%
142
 
1.0%
153
 
1.5%
165
2.5%
1712
6.0%
ValueCountFrequency (%)
491
 
0.5%
471
 
0.5%
451
 
0.5%
385
2.5%
376
3.0%

highway-mpg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.68656716
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:06.100098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.815149936
Coefficient of variation (CV)0.22208903
Kurtosis0.5611711398
Mean30.68656716
Median Absolute Deviation (MAD)5
Skewness0.5495071459
Sum6168
Variance46.44626866
MonotocityNot monotonic
2021-03-04T03:18:06.164940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2519
 
9.5%
2417
 
8.5%
3817
 
8.5%
3016
 
8.0%
3216
 
8.0%
3414
 
7.0%
3713
 
6.5%
2812
 
6.0%
2910
 
5.0%
339
 
4.5%
Other values (20)58
28.9%
ValueCountFrequency (%)
162
1.0%
171
0.5%
182
1.0%
192
1.0%
202
1.0%
ValueCountFrequency (%)
541
0.5%
531
0.5%
501
0.5%
472
1.0%
462
1.0%

price
Real number (ℝ≥0)

Distinct186
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13207.12935
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:06.239188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6189
Q17775
median10295
Q316500
95-th percentile32528
Maximum45400
Range40282
Interquartile range (IQR)8725

Descriptive statistics

Standard deviation7947.066342
Coefficient of variation (CV)0.601725487
Kurtosis3.231536887
Mean13207.12935
Median Absolute Deviation (MAD)3306
Skewness1.809675339
Sum2654633
Variance63155863.44
MonotocityNot monotonic
2021-03-04T03:18:06.314074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84952
 
1.0%
181502
 
1.0%
72952
 
1.0%
62292
 
1.0%
88452
 
1.0%
78982
 
1.0%
79572
 
1.0%
66922
 
1.0%
55722
 
1.0%
77752
 
1.0%
Other values (176)181
90.0%
ValueCountFrequency (%)
51181
0.5%
51511
0.5%
51951
0.5%
53481
0.5%
53891
0.5%
ValueCountFrequency (%)
454001
0.5%
413151
0.5%
409601
0.5%
370281
0.5%
368801
0.5%

city-L/100km
Real number (ℝ≥0)

HIGH CORRELATION

Distinct29
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.944145484
Minimum4.795918367
Maximum18.07692308
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-03-04T03:18:06.386722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4.795918367
5-th percentile6.351351351
Q17.833333333
median9.791666667
Q312.36842105
95-th percentile14.6875
Maximum18.07692308
Range13.28100471
Interquartile range (IQR)4.535087719

Descriptive statistics

Standard deviation2.534599261
Coefficient of variation (CV)0.254883566
Kurtosis-0.06511888838
Mean9.944145484
Median Absolute Deviation (MAD)1.958333333
Skewness0.5923833561
Sum1998.773242
Variance6.424193415
MonotocityNot monotonic
2021-03-04T03:18:06.457421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
7.58064516128
13.9%
12.3684210527
13.4%
9.79166666722
10.9%
8.70370370414
 
7.0%
10.217391312
 
6.0%
9.03846153812
 
6.0%
13.8235294112
 
6.0%
11.190476198
 
4.0%
9.48
 
4.0%
7.8333333338
 
4.0%
Other values (19)50
24.9%
ValueCountFrequency (%)
4.7959183671
 
0.5%
51
 
0.5%
5.2222222221
 
0.5%
6.1842105265
2.5%
6.3513513516
3.0%
ValueCountFrequency (%)
18.076923081
 
0.5%
16.785714292
 
1.0%
15.666666673
 
1.5%
14.68755
2.5%
13.8235294112
6.0%
Distinct3
Distinct (%)1.5%
Missing1
Missing (%)0.5%
Memory size1.7 KiB
Low
115 
Medium
62 
High
23 

Length

Max length6
Median length3
Mean length4.045
Min length3

Characters and Unicode

Total characters809
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium
ValueCountFrequency (%)
Low115
57.2%
Medium62
30.8%
High23
 
11.4%
(Missing)1
 
0.5%
2021-03-04T03:18:06.580068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:06.622685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
low115
57.5%
medium62
31.0%
high23
 
11.5%

Most occurring characters

ValueCountFrequency (%)
L115
14.2%
o115
14.2%
w115
14.2%
i85
10.5%
M62
7.7%
e62
7.7%
d62
7.7%
u62
7.7%
m62
7.7%
H23
 
2.8%
Other values (2)46
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter609
75.3%
Uppercase Letter200
 
24.7%

Most frequent character per category

ValueCountFrequency (%)
o115
18.9%
w115
18.9%
i85
14.0%
e62
10.2%
d62
10.2%
u62
10.2%
m62
10.2%
g23
 
3.8%
h23
 
3.8%
ValueCountFrequency (%)
L115
57.5%
M62
31.0%
H23
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Latin809
100.0%

Most frequent character per script

ValueCountFrequency (%)
L115
14.2%
o115
14.2%
w115
14.2%
i85
10.5%
M62
7.7%
e62
7.7%
d62
7.7%
u62
7.7%
m62
7.7%
H23
 
2.8%
Other values (2)46
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII809
100.0%

Most frequent character per block

ValueCountFrequency (%)
L115
14.2%
o115
14.2%
w115
14.2%
i85
10.5%
M62
7.7%
e62
7.7%
d62
7.7%
u62
7.7%
m62
7.7%
H23
 
2.8%
Other values (2)46
 
5.7%

diesel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
181 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters201
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0181
90.0%
120
 
10.0%
2021-03-04T03:18:06.737925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:06.776758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0181
90.0%
120
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0181
90.0%
120
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number201
100.0%

Most frequent character per category

ValueCountFrequency (%)
0181
90.0%
120
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common201
100.0%

Most frequent character per script

ValueCountFrequency (%)
0181
90.0%
120
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII201
100.0%

Most frequent character per block

ValueCountFrequency (%)
0181
90.0%
120
 
10.0%

gas
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
181 
0
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters201
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1181
90.0%
020
 
10.0%
2021-03-04T03:18:06.899675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-04T03:18:06.943965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1181
90.0%
020
 
10.0%

Most occurring characters

ValueCountFrequency (%)
1181
90.0%
020
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number201
100.0%

Most frequent character per category

ValueCountFrequency (%)
1181
90.0%
020
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common201
100.0%

Most frequent character per script

ValueCountFrequency (%)
1181
90.0%
020
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII201
100.0%

Most frequent character per block

ValueCountFrequency (%)
1181
90.0%
020
 
10.0%

Interactions

2021-03-04T03:17:43.940706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.010263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.070677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.128240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.213130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.272478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.342610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.400050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.459463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.514512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.574705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.630501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.684899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.749581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.810336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.871675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.929312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:44.989835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.208531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.290716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.358755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.421890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.489061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.551369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.615862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.675043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.739295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.798611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.859371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.924150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:45.989262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.058059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.122168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.183073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.247994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.312436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.378058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.440271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.503289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.565838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.629748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.697670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.762685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.822049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.882017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:46.946665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.012979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.087028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.152627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.212291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.275129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.337440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.483272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.551447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.616661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.679824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.745659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.805425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.869706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.927744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:47.985933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.047744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.111127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.175623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.236356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.300005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.367243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.433918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.499997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.565115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.629199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.692864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.759103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.825802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.894919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:48.958173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:49.020947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:49.087588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:49.155751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-04T03:17:55.836581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:55.890879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-04T03:17:56.525584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:56.582163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:56.642554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:56.697824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:56.752480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:56.806287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:56.862652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:56.914621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:56.971173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:57.023854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:57.083710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-04T03:17:57.258174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:57.322452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:57.387862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:57.452159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-04T03:17:58.289719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:58.354672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:58.420816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:58.482773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:58.548996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:58.614805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-04T03:17:58.746873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:58.810276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-04T03:17:59.203078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:59.266506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:59.332194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:17:59.400309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-04T03:17:59.670063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-04T03:18:00.000377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.066541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.127340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.193662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.254854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.316489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.382138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.449545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.514124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.573223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.635060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.696146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.756476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.819829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.891099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:00.951228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:01.009616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:01.071580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:01.127803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:01.188967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:01.245422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:01.302393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:01.363310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-04T03:18:01.425845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-04T03:18:07.187213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-04T03:18:07.567257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-04T03:18:07.718235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-04T03:18:07.878614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-04T03:18:01.587983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-04T03:18:02.119888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-04T03:18:02.243195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-04T03:18:02.317585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

symbolingnormalized-lossesmakeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgpricecity-L/100kmhorsepower-binneddieselgas
03122alfa-romerostdtwoconvertiblerwdfront88.60.8111480.89027848.82548dohcfour130mpfi3.472.689.0111.05000.0212713495.011.190476Medium01
13122alfa-romerostdtwoconvertiblerwdfront88.60.8111480.89027848.82548dohcfour130mpfi3.472.689.0111.05000.0212716500.011.190476Medium01
21122alfa-romerostdtwohatchbackrwdfront94.50.8226810.90972252.42823ohcvsix152mpfi2.683.479.0154.05000.0192616500.012.368421Medium01
32164audistdfoursedanfwdfront99.80.8486300.91944454.32337ohcfour109mpfi3.193.4010.0102.05500.0243013950.09.791667Medium01
42164audistdfoursedan4wdfront99.40.8486300.92222254.32824ohcfive136mpfi3.193.408.0115.05500.0182217450.013.055556Medium01
52122audistdtwosedanfwdfront99.80.8519940.92083353.12507ohcfive136mpfi3.193.408.5110.05500.0192515250.012.368421Medium01
61158audistdfoursedanfwdfront105.80.9259970.99166755.72844ohcfive136mpfi3.193.408.5110.05500.0192517710.012.368421Medium01
71122audistdfourwagonfwdfront105.80.9259970.99166755.72954ohcfive136mpfi3.193.408.5110.05500.0192518920.012.368421Medium01
81158auditurbofoursedanfwdfront105.80.9259970.99166755.93086ohcfive131mpfi3.133.408.3140.05500.0172023875.013.823529Medium01
92192bmwstdtwosedanrwdfront101.20.8495920.90000054.32395ohcfour108mpfi3.502.808.8101.05800.0232916430.010.217391Low01

Last rows

symbolingnormalized-lossesmakeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgpricecity-L/100kmhorsepower-binneddieselgas
191-174volvostdfourwagonrwdfront104.30.9072560.93333357.53034ohcfour141mpfi3.783.159.5114.05400.0232813415.010.217391Medium01
192-2103volvostdfoursedanrwdfront104.30.9072560.93333356.22935ohcfour141mpfi3.783.159.5114.05400.0242815985.09.791667Medium01
193-174volvostdfourwagonrwdfront104.30.9072560.93333357.53042ohcfour141mpfi3.783.159.5114.05400.0242816515.09.791667Medium01
194-2103volvoturbofoursedanrwdfront104.30.9072560.93333356.23045ohcfour130mpfi3.623.157.5162.05100.0172218420.013.823529High01
195-174volvoturbofourwagonrwdfront104.30.9072560.93333357.53157ohcfour130mpfi3.623.157.5162.05100.0172218950.013.823529High01
196-195volvostdfoursedanrwdfront109.10.9072560.95694455.52952ohcfour141mpfi3.783.159.5114.05400.0232816845.010.217391Medium01
197-195volvoturbofoursedanrwdfront109.10.9072560.95555655.53049ohcfour141mpfi3.783.158.7160.05300.0192519045.012.368421High01
198-195volvostdfoursedanrwdfront109.10.9072560.95694455.53012ohcvsix173mpfi3.582.878.8134.05500.0182321485.013.055556Medium01
199-195volvoturbofoursedanrwdfront109.10.9072560.95694455.53217ohcsix145idi3.013.4023.0106.04800.0262722470.09.038462Medium10
200-195volvoturbofoursedanrwdfront109.10.9072560.95694455.53062ohcfour141mpfi3.783.159.5114.05400.0192522625.012.368421Medium01